U.S. patent application number 12/054778 was filed with the patent office on 2009-10-01 for system and method to validate consistency of component business model maps.
Invention is credited to Anca-Andreea IVAN, Juhnyoung Lee, Yue Pan, Guo Tong Xie, Yang Yang.
Application Number | 20090248705 12/054778 |
Document ID | / |
Family ID | 41118686 |
Filed Date | 2009-10-01 |
United States Patent
Application |
20090248705 |
Kind Code |
A1 |
IVAN; Anca-Andreea ; et
al. |
October 1, 2009 |
SYSTEM AND METHOD TO VALIDATE CONSISTENCY OF COMPONENT BUSINESS
MODEL MAPS
Abstract
A system and method is described for using descriptive logic
(DL) representations to validate consistency in component business
model (CBM) maps. Semantic constraints are generated from a
semantic model of a component business model meta-model and
inconsistency conditions of CBM maps. The semantic model of the CBM
meta-model is applied to transform CBM maps into corresponding
semantic representations. An inference engine applies the semantic
constraints to the semantic representations to determine
inconsistencies between one CBM map and another and between a CBM
map and the component business model meta-model.
Inventors: |
IVAN; Anca-Andreea; (New
Rochelle, NY) ; Lee; Juhnyoung; (Yorktown Heights,
NY) ; Pan; Yue; (Beijing, CN) ; Xie; Guo
Tong; (Beijing, CN) ; Yang; Yang; (Beijing,
CN) |
Correspondence
Address: |
Whitham, Curtis, & Christofferson, P.C.,
11491 Sunset Hills Road, Suite 340
Reston
VA
20190
US
|
Family ID: |
41118686 |
Appl. No.: |
12/054778 |
Filed: |
March 25, 2008 |
Current U.S.
Class: |
1/1 ; 707/999.1;
707/E17.005 |
Current CPC
Class: |
G06Q 10/00 20130101 |
Class at
Publication: |
707/100 ;
707/E17.005 |
International
Class: |
G06F 17/30 20060101
G06F017/30 |
Claims
1. A method for automating the validation of consistency of
component business models, comprising: generating semantic
constraints from i) a semantic model of a component business model
meta-model and ii) inconsistency conditions applicable to at least
one component business model (CBM) maps; using said semantic model
to transform said at least one CBM maps into corresponding semantic
representations; and applying said semantic constraints to said
semantic representations to determine inconsistencies between said
component business model meta-model and one of said CBM maps.
2. The method of claim 1, wherein said component business model is
comprised of non-overlapping components arranged by accountability
level within non-overlapping managing concepts, wherein one of said
CBM maps is a map of a business enterprise, and wherein said
business enterprise operates in an industry and one of said CBM
maps is a map of said industry.
3. The method of claim 2, further comprising applying said semantic
constraints to said semantic representations to determine
inconsistencies between said enterprise CBM map and said industry
CBM map.
4. The method of claim 1, further comprising using a CBM tool to
modify said CBM enterprise map to remove said inconsistencies.
5. The method of claim 1, wherein Web Ontology Language (OWL) is
used to express said semantic model and said semantic
representations.
6. The method of claim 1, wherein said semantic constraints are
represented as a mixture of expressions and rule expressions in a
semantic markup language.
7. The method of claim 6, wherein said semantic markup language is
Resource Description Framework (RDF).
8. A system for automating the validation of consistency of
component business models, comprising: a component business model
representation of a business enterprise, further comprising a
component business model meta-model and at least one component
business model (CBM) maps of non-overlapping components arranged by
accountability level within non-overlapping managing concepts, one
of said at least one CBM maps being a map of said business
enterprise; a generator for generating semantic constraints from i)
a semantic model of said component business model meta-model and
ii) inconsistency conditions applicable to said at least one CBM
maps; a transformer for using said semantic model to transform said
CBM maps into corresponding semantic representations; and an
inference engine for applying said semantic constraints to said
semantic representations to determine inconsistencies between said
CBM meta-model and one of said CBM maps.
9. The system of claim 8, wherein said business enterprise operates
in an industry and one of said CBM maps is a map of said industry,
and wherein said inference engine determines inconsistencies
between said enterprise CBM map and said industry CBM map.
10. The system of claim 9, wherein said semantic representation of
the industry CBM map is a first ABox and said semantic
representation of the enterprise CBM map is a second ABox, and the
inference engine determines inconsistencies by performing a
self-consistency validation on a third ABox, said third ABox being
a combination of said first ABox and said second Abox.
11. The system of claim 9, wherein the component business model
meta-model is a TBox, and the inference engine determines
inconsistencies by verifying whether said first ABox complies with
said TBox and whether said second ABox complies with said TBox.
12. The system of claim 8, further comprising a CBM tool for using
said inconsistencies to modify said one of said CBM maps to remove
said inconsistencies.
13. The system of claim 13, wherein one of said CBM maps is a
universal CBM map, and wherein said semantic representation of said
universal CBM map is a fourth ABox A4.
14. The system of claim 19, wherein said inference engine
determines inconsistencies between said enterprise map and said
industry map and said universal map by performing a
self-consistency validation of a fifth ABox A5, said fifth ABox
being the combination A1+A2+A4.
15. Implementing a service for validating consistency of component
business models, comprising the method of: generating semantic
constraints from i) a semantic model of a component business model
meta-model and ii) inconsistency conditions applicable to at least
one component business model (CBM) maps; using said semantic model
to transform said at least one CBM maps into corresponding semantic
representations; and applying said semantic constraints to said
semantic representations to determine inconsistencies between i)
said component business model meta-model and one of said CBM maps
or ii) one of said CBM maps and another of said CBM maps.
16. A method for implementing a validation service as in claim 15,
wherein said component business model is comprised of
non-overlapping components arranged by accountability level within
non-overlapping managing concepts, wherein one of said CBM maps is
a map of a business enterprise, wherein said business enterprise
operates in an industry and one of said CBM maps is a map of said
industry, and wherein one of said CBM maps is a universal map from
which said industry CBM map is derived.
17. A method for implementing a validation service as in claim 15,
further comprising using a CBM tool to modify a CBM map to remove
said inconsistencies.
18. A computer implemented system for validating consistency of
component business models, comprising: first computer code for
generating semantic constraints from i) a semantic model of a
component business model meta-model and ii) inconsistency
conditions applicable to at least one component business model
(CBM) maps; second computer code for using said semantic model to
transform said at least one CBM maps into corresponding semantic
representations; and third computer code for applying said semantic
constraints to said semantic representations to determine
inconsistencies between said component business model meta-model
and one of said CBM maps.
19. A computer implemented validation system as in claim 18,
wherein said business enterprise operates in an industry and one of
said CBM maps in a map of said industry, further comprising fourth
computer code for applying said semantic constraints to said
semantic representations to determine inconsistencies between said
enterprise CBM map and said industry CBM map.
20. A computer implemented validation system as in claim 18,
further comprising fifth computer code for using a CBM tool to
modify said one of said CBM maps to remove said inconsistencies.
Description
DESCRIPTION
[0001] This invention is related to commonly owned U.S. patent
application Ser. No. 11/176,371 for "SYSTEM AND METHOD FOR
ALIGNMENT OF AN ENTERPRISE TO A COMPONENT BUSINESS MODEL" which is
incorporated by reference herein.
BACKGROUND OF THE INVENTION
[0002] 1. Field of the Invention
[0003] The present invention generally relates to component based
business models and, more particularly, to a system and method for
deducing and resolving potential inconsistencies in the semantic
representation of component business model maps.
[0004] 2. Background Description
[0005] Component Business Modeling is a state-of-art technology for
modeling the entire enterprise from a business perspective, driving
information technology (IT) solutions to help transform an
enterprise from a current AS-IS condition to a desired TO-BE
condition. The component business model (CBM) map is the key
component in CBM methodology and CBM related tools. Component
business modeling is a technique for modeling businesses based on a
number of non-overlapping "business components," which are defined
as relatively independent collections of business activities. It
provides simple business views for analysis, unlike traditional
business process-based models which provide transactional views of
businesses. The CBM methodology facilitates qualitative analysis
techniques such as the dependency analysis (to identify "hot"
components associated with business pain points), the heat map
analysis (also to identify "hot" components associated with
business pain points), and the overlay analysis (to identify IT
shortfalls of the "hot" components).
[0006] The CBM-based qualitative business analysis has been mostly
conducted manually by business consultants. What is needed for
automation of the CBM-based business analyses is a semantic
representation of the component business model. In particular,
there is a need to validate the CBM models by detecting
inconsistencies 1) among the various CBM maps (that is, the
universal CBM map at the broadest level, the intermediate level
industry CBM maps, and the CBM maps for particular enterprises) and
2) between the CBM meta-model and a CBM map.
[0007] Some examples of inconsistency are as follows. Suppose the
demand forecast and analysis component belongs to the marketing
competency in the CBM map for the retail industry. But a consultant
working on a CBM map for an enterprise within the retail industry
may assign the demand forecast and analysis component to a
different competency, say, financial management. Then the
enterprise map is inconsistent with the retail industry map, but
the consultant has no systematic methodology for identifying this
kind of inconsistency.
[0008] Another simple example would be the cardinality
inconsistency. For instance, the CBM meta-model specifies that a
component has one and only one accountability level. When working
on a CBM map, a consultant may give a component more than one
accountability level. This is not correct and will complicate
further analysis, but the consultant may not be aware of the
inconsistency because of the large number of components, activities
or services in one CBM map.
[0009] Inconsistencies in CBM maps will set traps that will
compromise the efficiency of further CBM related consulting. The
manual validation of CBM models and maps in order to avoid these
inconsistencies is a tedious and error-prone process, causing
significant degradation of productivity and accuracy of the
CBM-based analysis. Therefore, some methods or tools should be
developed to detect those inconsistencies as early as possible.
SUMMARY OF THE INVENTION
[0010] One aspect of the invention is a method for automating the
validation of consistency of component business models, comprising
generating semantic constraints from i) a semantic model of a
component business model meta-model and ii) inconsistency
conditions applicable to at least one component business model
(CBM) maps, using the semantic model to transform the CBM maps into
corresponding semantic representations, and applying the semantic
constraints to the semantic representations to determine
inconsistencies between the component business model meta-model and
one of the CBM maps. In this aspect of the invention it is
preferred that the component business model be comprised of
non-overlapping components arranged by accountability level within
non-overlapping managing concepts, wherein one of the CBM maps is a
map of a business enterprise, and one of the CBM maps is a map of
the industry in which the business enterprise operates.
[0011] In another aspect, the method of the invention applies the
semantic constraints to the semantic representations to determine
inconsistencies between the enterprise CBM map and the industry CBM
map. A further aspect of the invention's method is using a CBM tool
to modify the CBM enterprise map to remove the inconsistencies. It
is also an aspect of the invention to provide a service for
validating the consistency of CBM maps by using the method of the
invention.
[0012] It is also an aspect of the invention to provide a system
for automating the validation of consistency of component business
models, the system being comprised of a component business model
representation of a business enterprise, further comprising a
component business model meta-model and at least one CBM maps of
non-overlapping components arranged by accountability level within
non-overlapping managing concepts, one of the at least one CBM maps
being a map of the business enterprise, the system further being
comprised of a generator for generating semantic constraints from
i) a semantic model of the component business model meta-model and
ii) inconsistency conditions applicable to the at least one CBM
maps, the system further being comprised of a transformer for using
the semantic model to transform the CBM maps into corresponding
semantic representations, the system further being comprised of an
inference engine for applying the semantic constraints to the
semantic representations to determine inconsistencies between the
CBM meta-model and one of the CBM maps.
[0013] In another of its aspects, the system of the invention
provides that the semantic representation of the industry CBM map
is a first ABox and the semantic representation of the enterprise
CBM map is a second ABox, and the inference engine determines
inconsistencies by performing a self-consistency validation on a
third ABox, the third ABox being a combination of the first ABox
and the second Abox. In a further aspect of the invention the
component business model meta-model is a TBox, and the inference
engine determines inconsistencies by verifying whether the first
ABox complies with the TBox and whether the second ABox complies
with the TBox. In yet another aspect, one of the CBM maps is a
universal CBM map, and the semantic representation of the universal
CBM map is a fourth ABox A4. It is also an aspect of the system of
the invention for the inference engine to determine inconsistencies
between the enterprise map and the industry map and the universal
map by performing a self-consistency validation of a fifth ABox A5,
the fifth ABox being the combination A1+A2+A4. A further aspect of
the invention is a computer implementation in computer code of the
elements of the system.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] The foregoing and other objects, aspects and advantages will
be better understood from the following detailed description of a
preferred embodiment of the invention with reference to the
drawings, in which:
[0015] FIG. 1 is a flow chart showing how the overall logic of the
inconsistency detection process.
[0016] FIG. 1A is a schematic of a system implementing the
invention described in FIG. 1.
[0017] FIG. 2A is a diagram showing a semantic model of a fragment
of a CBM meta-model. FIG. 2B is a diagram showing a mapping between
the semantic model fragment of FIG. 2A and a representation of the
fragment using the Ecore meta-model.
[0018] FIG. 3 is a diagram showing a meta-model representation of a
"hasCompetency" definition.
[0019] FIG. 4 is a diagram showing a CBM meta-model representation
of a portion of a CBM retail industry map.
[0020] FIG. 5 is a diagram showing a graphical representation in
OWL of a portion of a CBM map for a particular enterprise within
the retail industry, corresponding to FIG. 4.
[0021] FIG. 6 is a diagram showing a graphical representation in
OWL of semantic constraints on cardinality.
DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT OF THE INVENTION
[0022] It is therefore a feature of the present invention to
provide systematic identification of inconsistencies in CBM
maps.
[0023] Another feature of the invention is automation of
identification of inconsistencies in CBM maps.
[0024] A further feature of the invention is to provide a method of
validating consistency of CBM maps.
[0025] It is also a feature of the invention to provide tools for
early detection of inconsistencies in CBM maps.
[0026] The present invention is a novel approach to detecting
inconsistencies in CBM maps based on semantic technologies. The
invention provides a semantic business model that uses a semantic
markup language to describe the CBM maps and the CBM meta-model.
For the purpose of illustrating the invention the OWL markup
language is used, but one skilled in the art will appreciate that
the same methodology can be used in other semantic markup languages
such as W3C and RDF.
[0027] Analysis of CBM maps using the semantic business model
discovers implicit facts in the analyses by using inference
capabilities of ontology by capturing relationships of relevant
concepts such as business components, business processes, business
activities, operational metrics, performance indicators, value
drivers, IT applications, IT capabilities (systems, services,
solutions, and the like), and resources including human resources.
A detailed structure of the semantic business model is captured in
the CBM meta-model.
[0028] The invention uses the Component Business Model (CBM)
described in related patent application Ser. No. 11/176,371 for
"SYSTEM AND METHOD FOR ALIGNMENT OF AN ENTERPRISE TO A COMPONENT
BUSINESS MODEL" (hereafter termed "the above referenced foundation
patent application"). CBM provides a logical and comprehensive view
of the enterprise, in terms that cut across commercial enterprises
in general and industries in particular. Typically, CBM presents
business information in the form of CBM maps at a universal level
(cutting across all industries), at an industry level (cutting
across all business within an industry) and at the level of a
particular enterprise within an industry. In principle, a map at a
lower level is a subset of, and therefore consistent with, a map at
a higher level.
[0029] The component business model as described in the above
referenced foundation patent application is based upon a logical
partitioning of business activities into non-overlapping managing
concepts, each managing concept being active at the three levels of
management accountability: providing direction to the business,
controlling how the business operates, and executing the operations
of the business. The term "managing concept" is specially defined
as described in the above referenced foundation patent application,
and is not literally a "managing concept" as that phrase would be
understood in the art. For the purpose of the present invention, as
for the related invention, "managing concept" is the term
associated with the following aspects of the partitioning
methodology. First, the methodology is a partitioning methodology.
The idea is to begin with a whole and partition the whole into
necessarily non-overlapping parts. Second, experience has shown
that the partitioning process works best when addressed to an asset
of the business. The asset can be further described by attributes.
Third, the managing concept must include mechanisms for doing
something commercially useful with the asset. For a sensibly
defined managing concept these mechanisms must cover the full range
of management accountability levels (i.e. direct, control and
execute). Managing concepts are further partitioned into
components, which are cohesive groups of activities. The boundaries
of a component usually fall within a single management
accountability level. It is important to emphasize that the
boundaries between managing concepts (and between components within
managing concepts) are logical rather than physical.
[0030] In order to detect inconsistencies in CBM maps, our approach
is to represent consistency conditions of CBM maps in Web Ontology
Language (OWL), and use the OWL inference engine to deduce the
potential inconsistencies of the semantic CBM representation of CBM
maps. This approach operates in the following manner: [0031] The
CBM meta-model is represented in OWL. [0032] Consistency conditions
are represented as further constraints, which can be OWL
expressions or OWL rule expressions, on the semantic CBM
meta-model. The base semantic CBM meta-model and further
constraints form semantic constraints on CBM maps. [0033] An OWL
inference engine takes the CBM meta-model with consistency
constraints as one input, and instance CBM maps as another input,
then deduces potential inconsistencies between one CBM map and
another CBM map or between CBM maps and the CBM meta-model after a
reasoning process.
[0034] OWL is based on a Description Logic (DL). In general, a
knowledge base expressed in a DL is constituted by two components.
The first component stores a set of universally quantified
assertions stating general properties of concepts and roles. The
second component comprises assertions on individual objects.
Traditionally, the first component is called TBox and the second
component is called ABox. A typical TBox assertion states that a
concept represents a specialization of another concept. A typical
ABox assertion is that a particular object is an instance of a
certain concept.
[0035] The central aspect of our approach is to transform CBM
inconsistency detection into a reasoning problem, and use OWL
representation to leverage its underlying DL computation
capability. In the DL, TBox is used to represent concepts,
relationships and their subsumption hierarchies. ABox in the DL is
used to represent instances of concepts and relationships.
[0036] Basically, there are two types of inconsistency problems.
The first issue is consistency among CBM maps, which can be
transformed into an ABox consistency issue. Suppose we have Retail
industry map as an ABox A1, and enterprise map for Acme is another
ABox A2, then we can use the DL inference engine to perform
self-consistency validation on A3, which is the combination of A1
and A2.
[0037] Another issue is consistency among CBM maps and the CBM
meta-model, which can be transformed into the consistency between
TBox and its ABox instances. Suppose we have an enterprise map for
Acme as an ABox A, and the CBM meta-model is the TBox T, then the
DL inference engine can reason and verify if A comply with the
definitions in T in a logical way.
[0038] Our approach has the following advantages: [0039] OWL
provides a sound and complete computational capability, which can
guarantee the results of a consistency check. [0040] An ontology is
more meaningful and easier to understand for business people.
[0041] A semantic model-based consistency check will improve the
correctness of various CBM-based qualitative business analyses,
including the dependency analysis, heat map analysis, and overlay
analysis.
[0042] Referring now to the drawings, and more particularly to FIG.
1, there is shown the overall working process of the inconsistency
detection and corresponding information flow and control flow.
[0043] Semantic Constraints Generator 115 will take Semantic Model
of CBM Meta-model 110 and Inconsistent Conditions of CBM Maps 125
as input, and generate a comprehensive set of Semantic Constraints
120 that are represented as a mixture of OWL expressions and OWL
rule expressions. CBM Maps 130 that are produced by other CBM tools
will be imported, and will be transformed to OWL facts by the OWL
Facts Transformer 140, using the Semantic Model of CBM Meta-model
110. The result of this transformation is CBM Maps in OWL 145. Then
the OWL Inference Engine 150 can take Semantic Constraints 120 and
CBM Maps in OWL 145 as input, and verify those constraints on CBM
maps by reasoning on the mixture of OWL expressions, OWL rule
expressions and OWL facts. Then OWL Inference Engine 150 can
generate Inconsistency Detection Result 160, which can be consumed
by other tools 170. It should be noted that each of the tools 170
may have its own meta-model to describe CBM maps.
[0044] FIG. 1A shows a system implementing the invention described
in FIG. 1. Semantic Constraints Generator 115 is implemented in
Generator computer program 115A, OWL Facts Transformer 140 is
implemented in Transformer computer program 140A, and OWL Inference
Engine 150 is implemented in Inference Engine computer program
150A. Inconsistency Conditions 125, Semantic Model 110, and CBM
Maps 130 comprise the input data 105 for the computer programs 155
that implement the invention. These inputs are typically created by
other programs (not shown). The computer programs 155 produce
outputs 135, comprised of Semantic Constraints 120, CBM Maps in OWL
145 and Inconsistency Detection Result 160. The programs 155,
inputs 105, and outputs 135 are stored on server 180, which is
connected to monitor and keyboard assembly 190. Those skilled in
the art will recognize that the implementation shown in FIG. 1A is
an exemplar implementation on a stand-alone device, and that the
operative functionality of programs 155 can be distributed in a
variety of configurations over local area and wide area
networks.
[0045] The following is a simple example to show how the entire
system works step by step.
Semantic Model of CBM Meta-Model
[0046] Here is a small portion of the semantic model of a CBM
meta-model, in OWL.
TABLE-US-00001 <owl:Class rdf:about="&emp;BusinessComponent
220"> </owl:Class> <owl:Class
rdf:about="&emp;BusinessCompetency 240"> </owl:Class>
<owl:ObjectProperty rdf:about="&emp;competency">
<rdfs:domain rdf:resource="&emp;BusinessComponent 220"/>
<rdfs:range rdf:resource="&emp;BusinessCompetency 240"/>
</owl:ObjectProperty> <owl:Class
rdf:about="&emp;BusinessService 260"> </owl:Class>
<owl:ObjectProperty rdf:about="&emp;usedService">
<rdfs:domain rdf:resource="&emp;BusinessComponent 220"/>
<rdfs:range rdf:resource="&emp;BusinessService 260"/>
</owl:ObjectProperty> <owl:Class
rdf:about="&emp;AccountabilityLevel 210"> </owl:Class>
<owl:ObjectProperty rdf:about="&emp;accountabilityLevel">
<rdfs:domain rdf:resource="&emp;BusinessComponnet 220"/>
<rdfs:range rdf:resource="&emp;AccountabilityLevel 210"/>
</owl:ObjectProperty>
[0047] FIG. 2A shows the graphical view of the above OWL fragment.
Business Component 220 has an Accountability Level 210 and has a
metric, shown as a key performance indicator (KPI 230). It also has
a business competency 240. Business Component 220 also has a
Business Process 250, which in turn has a Business Activity 270.
Business Process 250 is implemented in a Business Service 260.
[0048] Returning to FIG. 1, OWL Facts Transformer 140 transforms
CBM maps into OWL facts through mapping between OWL and other
modeling languages that are used to describe CBM maps in other CBM
tools 170. For example, Ecore is the meta-model included within the
Eclipse Modeling Framework. If Ecore, rather than OWL, is used to
represent the CBM meta-model, then the OWL Facts Transformer 140
uses a meta-model mapping between OWL and Ecore, as shown in FIG.
2B. Each of meta-model elements shown in FIG. 2A has a
corresponding element in the Ecore meta-model 200B, as shown by the
double-headed arrows in FIG. 2B: Accountability Level 210 maps to
Accountability Level 210B, Business Component 220 maps to Business
Component 220B, KPI 230 maps to KPI 230B, Business Competency 240
maps to Business Competency 240B, Business Process 250 maps to
Business Process 250B, Business Service 260 maps to Business
Service 260B, and Business Activity 270 maps to Business Activity
270B. Note that this is a one-to-one mapping that is operable in
both directions. Therefore, given this mapping between Ecore and
OWL, the OWL Facts Transformer 140 can transform CBM maps
represented in Ecore into OWL representations.
Inconsistency Conditions of CBM Map
[0049] Suppose there are two simple inconsistency conditions that a
CBM map should comply with. They cover two usage scenarios; (1)
consistency between an industry map and an enterprise map, and (2)
consistency between the CBM maps and the CBM meta-model.
[0050] Condition 1: As shown in FIG. 3 and the following OWL
fragment, if business component c 320 has competency p 310 in an
industry map, c 320 should also have competency p 310 in enterprise
map. This is represented by an OWL object property 330.
TABLE-US-00002 <owl:ObjectProperty
rdf:about="&emp;hasCompetency"> <rdf:type rdf:resource=
"&owl;FunctionalProperty"/> <rdfs:domain
rdf:resource="&emp;BusinessComponnet"/> <rdfs:range
rdf:resource="&emp;BusinessCompetency"/>
</owl:ObjectProperty>
CBM Map in OWL
[0051] For the purposes of the following illustrations, we will
consider ACME as an enterprise within the retail industry. As shown
in FIG. 4 and the following OWL fragment, the Retail industry map
will show that the business component
"Demand_Forecast_and_Analysis" 410 has the Marketing business
competency 420, the Marketing business competency 420 is different
from (owl:differentFrom 425) the FinancialManagement business
competency 430.
TABLE-US-00003 <BusinessComponent
rdf:ID="&emp;Demand_Forecast_and_Analysis">
<hasCompetency> <BusinessCompetency
rdf:ID="&emp;Marketing"> <owl:differentFrom>
<BusinessCompetency rdf:ID="&emp;FinancialManagement">
<owl:differentFrom rdf:resource="&emp;Marketing"/>
</BusinessCompetency> </owl:differentFrom>
</BusinessCompetency> </hasCompetency>
</BusinessComponent>
As shown in FIG. 5 and the following OWL fragment, however, the
Acme enterprise map shows that the business component
"Demand_Forecast_and_Analysis" 510 has the competency
FinancialManagement 520.
TABLE-US-00004 <BusinessComponent
rdf:ID="&emp;Demand_Forecast_and_Analysis">
<hasCompetency> <BusinessCompetency rdf:ID="&emp;
FinancialManagement "/> </hasCompetency>
</BusinessComponent>
OWL Inference Engine
[0052] The DL inference engine takes both the retail industry map
and ACME enterprise map as input to populate its ABox instances.
Because hasCompetency is a functional property, the DL reasoner can
deduce that the FinancialManagement competency 520 is the same as
(owl:sameAs) the Marketing competency 420. At the same time, these
two competencies are declared as different from each other
(owl:differentFrom 425). This generates a logic conflict under DL
model theory.
Inconsistency Detection Result
[0053] The DL inference engine can tell that there is an
inconsistency between the industry map (in this S example, the
Retail industry map) and the enterprise map (in this example, the
ACME enterprise map) on the definition of the demand forecast and
analysis component. Then a CBM tool (170 in FIG. 1) can read the
detection result and take proper actions to remind the
consultant.
[0054] Condition 2: As shown in FIG. 6 and in the OWL fragment
below, a business component c 630 has one and only one
accountability level. This is represented in OWL as a maximum
cardinality constraint 610 of one and a minimum cardinality
constraint 620 of one.
TABLE-US-00005 <owl:Class
rdf:about="&emp;BusinessComponnet"> <rdfs:subClassOf
rdf:resource="&emp; AcctMaxRes "/> <rdfs:subClassOf
rdf:resource="&emp; AcctMinRes "/> </owl:Class>
<owl:Restriction rdf:about="&emp; AcctMaxRes ">
<owl:onProperty rdf:resource="&emp;hasAcctLevel"/>
<owl:maxCardinality>1</owl:maxCardinality>
</owl:Restriction> <owl:Restriction rdf:about="&emp;
AcctMinRes "> <owl:onProperty
rdf:resource="&emp;hasAcctLevel"/>
<owl:minCardinality>1</owl:minCardinality>
</owl:Restriction>
[0055] Returning to FIG. 1, the inconsistency conditions of CBM
Maps 125 define the conditions that CBM maps should comply with,
which will be tested by OWL Inference Engine 150. Each
inconsistency condition can be decomposed as conjunctions or
disjunctions of simple conditions. By defining a mapping between
simple conditions and OWL restrictions, Semantic Constraints
Generator 115 can transform inconsistency conditions 125 into OWL
expressions.
[0056] For example, a condition "a business component should have
one and only one accountability level" is a conjunction of two
simple conditions: "a business component has at least one
accountability level" and "a business component has one
accountability level at most"; these simple conditions can be
transformed to minCardinality restriction 620 and maxCardinality
restrictions 610 in OWL, as shown in FIG. 6.
[0057] While the invention has been described in terms of a single
preferred embodiment, those skilled in the art will recognize that
the invention can be practiced with modification within the spirit
and scope of the appended claims.
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